Teams at Menlo Security Inc.

Build and scale low-latency AI serving infrastructure for SLMs, prompt-injection engines, and classification services. Architect GPU-accelerated inference pipelines, backend microservices, Kubernetes clusters, rate limiting, tenant guardrails, monitoring, and autoscaling systems. Develop continuous evaluation, retraining, model CI/CD, regression gates, and MLOps/LLMOps pipelines while improving service reliability, latency, cost efficiency, and detection quality.

Build and harden Chromium-based browser components in C++ for AI and browser security. Develop prompt-injection and Shadow AI detection, integrate AI agents, MCP servers, and CDP, and support networking and HTTP security capabilities. Build tooling and detection services with Python or Rust, deploy cloud infrastructure on AWS or GCP using Kubernetes and Terraform, and optimize high-scale systems across browser and backend environments.

Build and operate Menlo Security’s ingress proxy service, extending security controls to AI agent traffic. Own features from design through production while optimizing latency, throughput, concurrency, and reliability. Develop secure, maintainable systems in Rust or Go, modernize legacy components through staged migrations, and apply threat modeling, fuzzing, property-based testing, and integration testing. Collaborate across teams, provide technical leadership, communicate architectural decisions, and use AI tools thoughtfully.